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About this paper
Congjie Zheng, Chuanyi Xue, Bin Liang, Jun Yang, Changshui Zhang
SEAGym is an evaluation environment designed to assess self-evolving LLM agent harnesses by converting static benchmarks into dynamic sources for various operational contexts and diagnostic views. The framework revealed that the choice of harness update mechanism, batch size, and source diversity critically impacts an agent's generalization, stability, and transferability across different LLM backends and task distributions.
Key insights
- The specific mechanism of an agent's harness update (e.g., broad structural changes vs. skill accumulation) fundamentally dictates its learning dynamics, generalization capabilities, and stability.
- Training parameters like batch size have a non-monotonic effect on performance, and source diversity is crucial for robust harness evolution, helping recovery from harmful updates and fostering broader generalization.
- Harness gains are often specific to the failure patterns exposed during training, leading to unstable or asymmetric transfer across different LLM backends and task distributions, especially for out-of-domain tasks.
Summary by alphaXiv.
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